Addressing the multifaceted and growing optimization challenges in various fields, including renewable energy, structural design, and large-scale industrial operations, necessitates continuous refinement of metaheuristic algorithms. The Beaver Behavior Optimizer (BBO) has recently been proposed as a competitive swarm intelligence approach. However, the original BBO mechanism still exhibits tendencies toward stagnation in high-dimensional and complex local optima landscapes due to fixed update rules. To elevate robustness and solution quality, this paper introduces an enhanced Beaver Behavior Optimizer (CCBBO), which suppresses structural bias by integrating a mathematical Crisscross-Strategy (CC). The CC mechanism, comprising Horizontal Crossover Search (HCS) and Vertical Crossover Search (VCS), strategically promotes non-linear and comprehensive information exchange across solution dimensions. This integration enables CCBBO to explore the search space more thoroughly and perform exploitation more precisely than the original BBO. The performance of CCBBO is rigorously substantiated through extensive experiments on the CEC2017 benchmark suite. The results decisively demonstrate that CCBBO achieves the best overall performance with the lowest average Friedman rank of 1.5517, significantly outperforming the original BBO (rank 2.8966) and eight other state-of-the-art optimizers. Furthermore, CCBBO is applied to a 60-dimensional real-world oil reservoir production optimization problem. Comparative analysis reveals that CCBBO consistently achieves a significantly higher mean Net Present Value (NPV) of 9.512 × 108 USD and the lowest standard deviation of 1.481 × 107 USD under identical constraints, confirming its status as a robust and stable optimization tool for tackling complex decision-making problems in engineering domains.
Huang et al. (2026) studied this question.